Learning a variational network for reconstruction of accelerated MRI data
Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …
data by learning a variational network that combines the mathematical structure of …
Deep joint demosaicking and denoising
Demosaicking and denoising are the key first stages of the digital imaging pipeline but they
are also a severely ill-posed problem that infers three color values per pixel from a single …
are also a severely ill-posed problem that infers three color values per pixel from a single …
Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
While variational methods have been among the most powerful tools for solving linear
inverse problems in imaging, deep (convolutional) neural networks have recently taken the …
inverse problems in imaging, deep (convolutional) neural networks have recently taken the …
Deep learning for camera data acquisition, control, and image estimation
We review the impact of deep-learning technologies on camera architecture. The function of
a camera is first to capture visual information and second to form an image. Conventionally …
a camera is first to capture visual information and second to form an image. Conventionally …
End-to-end learning for joint image demosaicing, denoising and super-resolution
Image denoising, demosaicing and super-resolution are key problems of image restoration
well studied in the recent decades. Often, in practice, one has to solve these problems …
well studied in the recent decades. Often, in practice, one has to solve these problems …
A review of an old dilemma: Demosaicking first, or denoising first?
Image denoising and demosaicking are the first two crucial steps in digital camera pipelines.
In most of the literature, denoising and demosaicking are treated as two independent …
In most of the literature, denoising and demosaicking are treated as two independent …
Joint demosaicing and denoising with self guidance
Usually located at the very early stages of the computational photography pipeline,
demosaicing and denoising play important parts in the modern camera image processing …
demosaicing and denoising play important parts in the modern camera image processing …
Variational networks: connecting variational methods and deep learning
In this paper, we introduce variational networks (VNs) for image reconstruction. VNs are fully
learned models based on the framework of incremental proximal gradient methods. They …
learned models based on the framework of incremental proximal gradient methods. They …